修改部分错误的文档，加长了历史记录（for 32k Models） (#1629)
* 更新上agent提示词代码 * 更新部分文档，修复了issue中提到的bge匹配超过1 的bug * 按需修改
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README.md
23
README.md
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@ -167,6 +167,9 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
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- [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh)
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- [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh)
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- [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh)
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- [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5)
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- [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5)- [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5)
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- [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5)
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- [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct)
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- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-base-zh)
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- [sensenova/piccolo-large-zh](https://huggingface.co/sensenova/piccolo-large-zh)
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@ -274,28 +277,20 @@ $ git clone https://huggingface.co/moka-ai/m3e-base
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在开始执行 Web UI 或命令行交互前，请先检查 [configs/model_config.py](configs/model_config.py) 和 [configs/server_config.py](configs/server_config.py) 中的各项模型参数设计是否符合需求：
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- 请确认已下载至本地的 LLM 模型本地存储路径写在 `llm_model_dict` 对应模型的 `local_model_path` 属性中，如:
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```
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"chatglm2-6b": "/Users/xxx/Downloads/chatglm2-6b",
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```python
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llm_model_dict={
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"chatglm2-6b": {
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"local_model_path": "/Users/xxx/Downloads/chatglm2-6b",
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"api_base_url": "http://localhost:8888/v1", # "name"修改为 FastChat 服务中的"api_base_url"
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"api_key": "EMPTY"
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},
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}
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```
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- 请确认已下载至本地的 Embedding 模型本地存储路径写在 `embedding_model_dict` 对应模型位置，如：
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```python
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embedding_model_dict = {
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"m3e-base": "/Users/xxx/Downloads/m3e-base",
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}
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```
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"m3e-base": "/Users/xxx/Downloads/m3e-base",
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```
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- 请确认本地分词器路径是否已经填写，如：
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```python
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```
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text_splitter_dict = {
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"ChineseRecursiveTextSplitter": {
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"source": "huggingface", ## 选择tiktoken则使用openai的方法,不填写则默认为字符长度切割方法。
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@ -358,7 +353,7 @@ $ python startup.py --all-webui --model-name Qwen-7B-Chat
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```python
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gpus=None,
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num_gpus=1,
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num_gpus= 1,
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max_gpu_memory="20GiB"
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```
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41
README_en.md
41
README_en.md
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@ -160,6 +160,8 @@ Following models are tested by developers with Embedding class of [HuggingFace](
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- [moka-ai/m3e-large](https://huggingface.co/moka-ai/m3e-large)
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- [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh)
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- [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh)
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- [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5)
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- [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5)
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- [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh)
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- [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct)
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- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-base-zh)
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@ -228,30 +230,33 @@ $ git clone https://huggingface.co/moka-ai/m3e-base
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```
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### 3. Setting Configuration
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Copy the model-related parameter configuration template file [configs/model_config.py.example](configs/model_config.py.example) and store it under the project path `. /configs` path and rename it `model_config.py`.
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Copy the model-related parameter configuration template file [configs/model_config.py.example](configs/model_config.py.example) and save it in the `./configs` path under the project path, and rename it to `model_config.py`.
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Copy the service-related parameter configuration template file [configs/server_config.py.example](configs/server_config.py.example) and store it under the project path `. /configs` path and rename it `server_config.py`.
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Copy the service-related parameter configuration template file [configs/server_config.py.example](configs/server_config.py.example) to save in the `./configs` path under the project path, and rename it to `server_config.py`.
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Before you start executing the Web UI or command line interactions, check that each of the items in [configs/model_config.py](configs/model_config.py) and [configs/server_config.py](configs/server_config.py) The model parameters are designed to meet the requirements:
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Before starting to execute Web UI or command line interaction, please check whether each model parameter in `configs/model_config.py` and `configs/server_config.py` meets the requirements.
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- Please make sure that the local storage path of the downloaded LLM model is written in the `local_model_path` attribute of the corresponding model in `llm_model_dict`, e.g..
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```
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"chatglm2-6b":"/Users/xxx/Downloads/chatglm2-6b",
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* Please confirm that the path to local LLM model and embedding model have been written in `llm_dict` of `configs/model_config.py`, here is an example:
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* If you choose to use OpenAI's Embedding model, please write the model's ``key`` into `embedding_model_dict`. To use this model, you need to be able to access the OpenAI official API, or set up a proxy.
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```python
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llm_model_dict={
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"chatglm2-6b": {
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"local_model_path": "/Users/xxx/Downloads/chatglm2-6b",
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"api_base_url": "http://localhost:8888/v1", # "name"修改为 FastChat 服务中的"api_base_url"
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"api_key": "EMPTY"
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},
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}
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```
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```python
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embedding_model_dict = {
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"m3e-base": "/Users/xxx/Downloads/m3e-base",
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}
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- Please make sure that the local storage path of the downloaded Embedding model is written in `embedding_model_dict` corresponding to the model location, e.g.:
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```
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"m3e-base":"/Users/xxx/Downloads/m3e-base", ``` Please make sure that the local storage path of the downloaded Embedding model is written in the location of the corresponding model, e.g.
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```
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- Please make sure that the local participle path is filled in, e.g.:
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```
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text_splitter_dict = {
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"ChineseRecursiveTextSplitter": {
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"source": "huggingface", ## Select tiktoken to use openai's method, don't fill it in then it defaults to character length cutting method.
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"tokenizer_name_or_path": "", ## Leave blank to use the big model of the tokeniser.
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}
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}
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```
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### 4. Knowledge Base Migration
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@ -30,6 +30,8 @@ MODEL_PATH = {
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"bge-base-zh": "BAAI/bge-base-zh",
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"bge-large-zh": "BAAI/bge-large-zh",
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"bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
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"bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
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"bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
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"piccolo-base-zh": "sensenova/piccolo-base-zh",
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"piccolo-large-zh": "sensenova/piccolo-large-zh",
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"text-embedding-ada-002": "your OPENAI_API_KEY",
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@ -133,7 +133,7 @@ class EmbeddingsPool(CachePool):
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else:
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# maybe ReRanker or else, just use empty string instead
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query_instruction = ""
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embeddings = HuggingFaceBgeEmbeddings(model_name=embedding_model_dict[model],
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embeddings = HuggingFaceBgeEmbeddings(model_name=get_model_path(model),
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model_kwargs={'device': device},
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query_instruction=query_instruction)
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if model == "bge-large-zh-noinstruct": # bge large -noinstruct embedding
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@ -101,8 +101,10 @@ def dialogue_page(api: ApiRequest):
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st.success(msg)
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st.session_state["prev_llm_model"] = llm_model
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temperature = st.slider("Temperature：", 0.0, 1.0, TEMPERATURE, 0.05)
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history_len = st.number_input("历史对话轮数：", 0, 10, HISTORY_LEN)
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temperature = st.slider("Temperature：", 0.0, 1.0, TEMPERATURE, 0.01)
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## 部分模型可以超过10抡对话
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history_len = st.number_input("历史对话轮数：", 0, 20, HISTORY_LEN)
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def on_kb_change():
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st.toast(f"已加载知识库： {st.session_state.selected_kb}")
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@ -117,7 +119,10 @@ def dialogue_page(api: ApiRequest):
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key="selected_kb",
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)
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kb_top_k = st.number_input("匹配知识条数：", 1, 20, VECTOR_SEARCH_TOP_K)
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## Bge 模型会超过1
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score_threshold = st.slider("知识匹配分数阈值：", 0.0, 1.0, float(SCORE_THRESHOLD), 0.01)
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# chunk_content = st.checkbox("关联上下文", False, disabled=True)
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# chunk_size = st.slider("关联长度：", 0, 500, 250, disabled=True)
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elif dialogue_mode == "搜索引擎问答":
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